Gopal Krishna R Dhondalay PhD

Summary

​

The quality of cells for whole-cell vaccination therapy depends on the causal effect of cell preparation parameters. We selected prostate cancer P4E6 cell lines for the investigation. To investigate the influence of engineering causal factors on cell preparation such as Shear speed, Time, Passage Number, Generation number, Doubling time, Hold time and Time between passages, and as the representation of cell quality, we also investigated cell membrane integrity and cell surface markers such as CD9. CD147 and HLA A-C. We adopted an ultra-scale-down strategy for the early characterization of parameters. We used Artificial Neural Network (ANN) based model approach wherein, 50 independent feed-forward and error back-propagated multi-layer predictive ANN models were implemented.

​

All causal factors studied were found to be significant in establishing an ANN model for the prediction of cell quality parameters with the extent of exposure to shear stress being the most significant and then passage number (range 57–66) and generation number (range 10–19) determining most strongly the cells’ resistance to shear stress. Both the operation of the final cell passage and the hold time of the cells in a formulation buffer also determine the cells’ resistance to shear stress. The processing parameters related to cell handling after preparation, i.e., shear stress and time of exposure were found to be the most influential affecting cell quality.

​

CD9 surface marker loss was the most sensitive indicator of the effects of shear stress followed by loss of membrane integrity and then HLA A-C, while CD147 remained unaffected by shear stress or even prone to increase. Also, greater stability of cell surface marker presence was noted for cells generated at greater passage numbers or generation numbers or for a reduction in hold time in the formulation buffer.

​

A publication in the journal of Artificial Intelligence in Medicine was published based on this project.

​